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Overlapping community detection in networks based on Neutrosophic theory

Author

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  • Gholami, Maryam
  • Sheikhahmadi, Amir
  • Khamforoosh, Keyhan
  • Jalili, Mahdi

Abstract

Discovering community structure is one of the most intensively studied problems in network science. Many real networks are composed of nodes belonging to multiple communities. In this manuscript, a new overlapping community detection algorithm is proposed based on neutrosophic set (NS) theory. The proposed community detection method manages uncertainty arisen from imprecise definition of communities, by handling boundary and outlier nodes. In the first step, the proposed algorithm calculates the dissimilarity index between each pair of nodes in the network. Then, in order to keep the original distance between nodes as much as possible, the network structure is mapped into a low-dimensional space by multidimensional scaling. Finally, the neutrosophic c-means algorithm is employed to find communities in the network. The experimental results show that the proposed algorithm can detect communities on real and artificial datasets effectively and accurately.

Suggested Citation

  • Gholami, Maryam & Sheikhahmadi, Amir & Khamforoosh, Keyhan & Jalili, Mahdi, 2022. "Overlapping community detection in networks based on Neutrosophic theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 598(C).
    • Handle: RePEc:eee:phsmap:v:598:y:2022:i:c:s0378437122002813
    DOI: 10.1016/j.physa.2022.127359
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    References listed on IDEAS

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    1. Liu, Qian & Wang, Jian & Zhao, Zhidan & Zhao, Na, 2022. "Relatively important nodes mining algorithm based on community detection and biased random walk with restart," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).

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